The development and use of pattern recognition generally involves machine learning to recognize patterns in data by virtue, for example, of data regularities and/or irregularities. For example, a pattern recognition system may be “trained” to properly identify patterns by using training data that has pre-identified the relevant classes of the data points and/or may be used to recognize patterns in a previously unclassified set of data. One of the most common techniques for pattern recognition involves the use of Artificial Neural Networks (ANN), or simply neural networks, which were initially modeled after biological neural systems. Artificial Neural Networks are capable of learning and solving highly non-linear pattern recognition problems. In doing so, such networks typically involve the use of transcendental functions (e.g., sigmoid functions, hyperbolic tangent functions, etc.) and/or memory-intensive algorithms. Accordingly, the use of neural networks for pattern recognition is often limited or non-existent with wearable computing devices, low-power devices, and/or other hardware-limited devices (e.g., devices without a dedicated acceleration unit suitable for pattern recognition).